﻿* Encoding: UTF-8.


GET 
  FILE='/Users/Alex/Dropbox/Dissertation.2/Number of Surveys/Data/ANES 2012/Base File/anes_timeseries_2012.sav'. 
 ALTER TYPE ALL(A=AMIN).
DATASET NAME DataSet1 WINDOW=FRONT.


***MODEL VARIABLES

*WEIGHTS

DESCRIPTIVES weight_ftf weight_web weight_full.

WEIGHT BY weight_web.

Freq wave_completions.
Freq mode.

**MAIN INDEPENDENT VARIABLE

Freq profile_surveyscompleted.
MISSING VALUES profile_surveyscompleted (-1).
Freq profile_surveyscompleted.
COMPUTE NumberSurveys = profile_surveyscompleted.
Freq NumberSurveys.


***CONTROL VARIABLES


Freq gender_respondent_x.
RECODE gender_respondent_x (1=0) (2=1) INTO female.
Freq female.

Freq dem_age_r_x.
COMPUTE age = dem_age_r_x.
MISSING VALUES age (-2).
Freq age.
RECODE age (17 THRU 30=1) (31 THRU 45 =2) (46 THRU 60 = 3) (61 THRU HI = 4) INTO agecats. 
Freq agecats.
VALUE LABELS
agecats
1 ' 18-30'
2 '31-45'
3 '46-60'
4 '61+'.
Freq agecats

Freq dem_marital.
RECODE dem_marital (1 THRU 2 = 1) (3=2) (4=3) (5=4) (6=5) INTO maritalstatus.
VALUE LABELS
maritalstatus
1 'Married'
2 'Widowed'
3 'Divorced'
4 'Separated'
5 'Never married'.
Freq maritalstatus.
RECODE maritalstatus (1=1) (2 THRU 5=0) INTO married.
Freq married.

Freq dem_edugroup_x.
COMPUTE educ = dem_edugroup_x.
MISSING VALUES educ (-9, -2).
VALUE LABELS
educ
1 'LT HS'
2 'HS credential'
3 'Some post-high school'
4 'Bachelors degree'
5 'Graduate degree'.
Freq educ.

Freq dem_empstatus_1digitfin_x.
RECODE dem_empstatus_1digitfin_x (1=1) (2 THRU 4=2) (5=3) (6=4) (7=5) (8=6) INTO employment.
VALUE LABELS 
employment
1 'Working gt 20 hrs/wk'
2 'Unemployed'
3 'Retired'
4 'Permanently disabled'
5 'Homemaker'
6 'Student'.
Freq employment.

RECODE employment (1=1) (2 THRU 6 = 0) INTO Working.
Freq Working. 

RECODE employment (1=1) (2 THRU 6 = 0) INTO WorkingGT20Hours.
Freq WorkingGT20Hours.
RECODE employment (1=0) (2 =1) (3 THRU 6 = 0) INTO WUnemployed.
RECODE employment (1 THRU 2 =0) (3 =1) (4 THRU 6 = 0) INTO WRetired.
RECODE employment (1 THRU 3 =0) (4 =1) (5 THRU 6 = 0) INTO WPermanentlyDisabled.
RECODE employment (1 THRU 4 =0) (5 =1) (5 THRU 6 = 0) INTO WHomemaker.
RECODE employment (1 THRU 5 =0) (6 =1)  INTO WStudent.
Freq WorkingGT20Hours WUnemployed WRetired WPermanentlyDisabled WHomemaker WStudent.

Freq  dem_raceeth_x.
COMPUTE raceethnic = dem_raceeth_x.
VALUE LABELS
raceethnic 
1 'White'
2 'Black'
3 'Asian'
4 'Native'
5 'Hispanic'
6 'Other'.
MISSING VALUES raceethnic (-9).
Freq raceethnic.
RECODE raceethnic (1=1) (2 THRU 6 = 0) INTO white.
Freq white.
ONEWAY NumberSurveys BY raceethnic
  /STATISTICS DESCRIPTIVES
  /MISSING ANALYSIS
  /POSTHOC=BONFERRONI ALPHA(0.05).

Freq dem2_inethome.
RECODE dem2_inethome (1=1) (2=0) INTO Internet.
VALUE LABELS
Internet
0 'No'
1 'Yes'.
Freq Internet.
T-TEST GROUPS= Internet(0 1) 
  /MISSING=ANALYSIS 
  /VARIABLES=NumberSurveys
  /CRITERIA=CI(.95).

Freq dem2_landline dem2_cellnum.
RECODE dem2_landline dem2_cellnum (0=0) (1 THRU HI = 1) INTO HHLandline HHCellphone.
VALUE LABELS 
HHLandline HHCellphone
0 'No'
1 'Yes'.
Freq HHLandline HHCellphone.
T-TEST GROUPS=HHLandline(0 1) 
  /MISSING=ANALYSIS 
  /VARIABLES=NumberSurveys
  /CRITERIA=CI(.95).
T-TEST GROUPS= HHCellphone(0 1) 
  /MISSING=ANALYSIS 
  /VARIABLES=NumberSurveys
  /CRITERIA=CI(.95).

Freq inc_incgroup_pre incgroup_prepost_x.
COMPUTE incomePre = inc_incgroup_pre.
COMPUTE incomePost = incgroup_prepost_x.
VALUE LABELS
incomePre incomePost
1 '<5K'
2 '5-10'
3 '10-12.5'
4 '12.5-15'
5 '15-17.5'
6 '17.5-20'
7 '20-22.5'
8 '22.5-25'
9 '25-27.5'
10 '27.5-30'
11 '30-35'
12 '35-40'
13 '40-45'
14 '45-50'
15 '50-55'
16 '55-60'
17 '60-65'
18 '65-70'
19 '70-75'
20 '75-80'
21 '80-90'
22 '90-100'
23 '100-110'
24 '110-125'
25 '125-150'
26 '150-175'
27 '175-250'
28 '>250'.
MISSING VALUES incomePre incomePost (-9, -8, -2).
Freq incomePre incomePost.
RECODE incomePre (1 THRU 10=1) (11 THRU 16=2) (17 THRU 22 = 3) (23 THRU HI = 4) INTO incomepretrunc.
VALUE LABELS 
incomepretrunc
1 '0-29.9k'
2 '30-59.9k'
3 '60-99.9k'
4 '100+k'.
Freq incomepretrunc.


Freq pid_x.
MISSING VALUES pid_x (-2).
RECODE pid_x (1 THRU 3 = 1) (4=2) (5 THRU 7 =3) INTO pid3.
VALUE LABELS
pid3
1 'Democrat'
2 'Independent'
3 'Republican'.
RECODE pid_x (1 THRU 3 =1) (4 THRU 7 =0) INTO Dem.
RECODE pid_x (1 THRU 3 =0) (4=1) (5 THRU 7 =0) INTO Indep.
RECODE pid_x (1 THRU 4 =0) (5 THRU 7 =1) INTO Rep.
Freq Dem Indep Rep pid_x.

Freq libcpre_self.
MISSING VALUES libcpre_self (-9,-8,-2).
Freq libcpre_self.
RECODE libcpre_self (1 THRU 3 =1) (4=2) (5 THRU 7 = 3) INTO libcon3.
VALUE LABELS 
libcon3 
1 'Liberal'
2 'Moderate'
3 'Conservative'.
Freq libcon3.
RECODE libcon3 (1=1) (2=0) (3=0) INTO Lib.
RECODE libcon3 (1=0) (2=1) (3=0) INTO Mod.
RECODE libcon3 (1=0) (2=0) (3=1) INTO Cons.
Freq Lib Mod Cons.

Freq interest_attention.
MISSING VALUES interest_attention (-9, -8).
RECODE interest_attention (1=5) (2=4) (3=3) (4=2) (5=1) INTO InterestPolitics.
VALUE LABELS
InterestPolitics
1 'Never'
2 'Some'
3 'About half'
4 'Most times'
5 'Always'.
Freq InterestPolitics.

***SUBSTANTIVE VARIABLES ANALYSIS

Freq female agecats married educ working white Internet HHLandline incomePre pid_x libcpre_self.

NumberSurveys MonthsInPanel NumberSurveysPerMonth



*	1	interest_attention	PRE: How often does R pay attn to politics and elections	Likert

Freq interest_attention.
MISSING VALUES interest_attention (-9, -8).
RECODE interest_attention (1=5) (2=4) (3=3) (4=2) (5=1) INTO InterestPolitics.
VALUE LABELS
InterestPolitics
1 'Never'
2 'Some'
3 'About half'
4 'Most times'
5 'Always'.
Freq InterestPolitics.

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT InterestPolitics
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	2	interest_following	PRE: Interested in following campaigns standard	Likert

Freq interest_following.
RECODE interest_following (1=3) (2=2) (3=1) INTO interest_followingRC.
VALUE LABELS 
interest_followingRC
1 'Not much interested'
2 'Somewhat interested'
3 'Very interested'.
Freq interest_followingRC. 

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT interest_followingRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.


*	3	interest_wherevote	PRE: Does R know where to go to vote in neighborhood	Dichotomous

Freq interest_wherevote.
MISSING VALUES interest_wherevote (-9,-8,3).
RECODE interest_wherevote (1=1) (2=0) INTO KnowWhereVote.

LOGISTIC REGRESSION VARIABLES KnowWhereVote
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons 
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).


*	4	interest_voted2008	PRE: Did R vote for President in 2008	Dichotomous
MonthsInPanel NumberSurveysPerMonth
Freq interest_voted2008.
MISSING VALUES interest_voted2008 (-8,-9).
RECODE interest_voted2008 (1=1) (2=0) INTO Voted2008.

LOGISTIC REGRESSION VARIABLES Voted2008
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

*	5	prmedia_wkinews	PRE: Days in typical week review news on internet	Likert
*	6	prmedia_atinews	PRE: Attention to internet news	Likert
*	7	prmedia_wktvnws	PRE: Days in typical week watch news on TV	Likert
*	8	prmedia_attvnews	PRE: Attention to TV news	Likert
*	9	prmedia_wkpaprnws	PRE: Days in typical wk read news in print newspaper	Likert
*	10	prmedia_atpprnews	PRE: Attention to printed newspaper news	Likert
*	11	prmedia_wkrdnws	PRE: Days in typical week listen news on radio	Likert
*	12	prmedia_atrdnews	PRE: Attention to radio news	Likert

Freq prmedia_wkinews  prmedia_wktvnws prmedia_wkpaprnws  prmedia_wkrdnws .
MISSING VALUES prmedia_wkinews  prmedia_wktvnws prmedia_wkpaprnws  prmedia_wkrdnws (-9 THRU -1).

Freq prmedia_atinews prmedia_attvnews prmedia_atpprnews prmedia_atrdnews.
RECODE prmedia_atinews prmedia_attvnews prmedia_atpprnews prmedia_atrdnews (1=5) (2=4) (3=3) 
(4=2) (5=1) INTO prmedia_atinewsRC prmedia_attvnewsRC prmedia_atpprnewsRC prmedia_atrdnewsRC.
VALUE LABELS 
prmedia_atinewsRC prmedia_attvnewsRC prmedia_atpprnewsRC prmedia_atrdnewsRC
1 'None at all'
2 'A little'
3 'A moderate amount'
4 'A lot'
5 'A great deal'.
Freq prmedia_atinewsRC prmedia_attvnewsRC prmedia_atpprnewsRC prmedia_atrdnewsRC.

*5 

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT prmedia_wkinews
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*6

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT prmedia_atinewsRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*7

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT prmedia_wktvnws
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*8

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT prmedia_attvnewsRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*9

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT prmedia_wkpaprnws
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*10

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT prmedia_atpprnewsRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*11

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT prmedia_wkrdnws
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*12

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT prmedia_atrdnewsRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	13	prevote_intpres	PRE: Does R intend to vote for President	Dichotomous
*	14	prevote_intpresst	PRE: Pref strng for Pres cand for whom R intends to vote	Dichotomous
*	15	prevote_inths	PRE: Does R intend to vote for U.S. House	Dichotomous
*	16	prevote_intsen	PRE: Does R intend to vote for U.S. Senate	Dichotomous

Freq prevote_intpresst prevote_intpres prevote_inths prevote_intsen.
RECODE prevote_intpresst prevote_intpres prevote_inths prevote_intsen (1=1) (2=0) INTO
prevote_intpresstRC prevote_intpresRC prevote_inthsRC prevote_intsenRC.
Freq prevote_intpresstRC prevote_intpresRC prevote_inthsRC prevote_intsenRC.

*13

LOGISTIC REGRESSION VARIABLES prevote_intpresstRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons 
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

*14

LOGISTIC REGRESSION VARIABLES prevote_intpresRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons 
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

*15

LOGISTIC REGRESSION VARIABLES prevote_inthsRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons 
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

*16

LOGISTIC REGRESSION VARIABLES prevote_intsenRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons 
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

*	17	candlik_likedpc	PRE: Is there anything R likes about Democratic Pres cand	Dichotomous
*	18	candlik_disldpc	PRE: Is there anything R dislikes about Democratic Pres cand	Dichotomous
*	19	candlik_likerpc	PRE: Is there anything R likes about Republican Pres cand	Dichotomous
*	20	candlik_dislrpc	PRE: Is there anything R dislikes about Republican Pres cand	Dichotomous

Freq candlik_likedpc  candlik_disldpc candlik_likerpc  candlik_dislrpc .
MISSING VALUES candlik_likedpc  candlik_disldpc candlik_likerpc  candlik_dislrpc (-9,-8).
RECODE candlik_likedpc  candlik_disldpc candlik_likerpc  candlik_dislrpc (1=1) (2=0)
INTO candlik_likedpcRC  candlik_disldpcRC candlik_likerpcRC  candlik_dislrpcRC.
VALUE LABELS
candlik_likedpcRC  candlik_disldpcRC candlik_likerpcRC  candlik_dislrpcRC
0 'No'
1 'Yes'.
Freq candlik_likedpcRC  candlik_disldpcRC candlik_likerpcRC  candlik_dislrpcRC.

*17

LOGISTIC REGRESSION VARIABLES candlik_likedpcRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons 
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

*18

LOGISTIC REGRESSION VARIABLES candlik_disldpcRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons 
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

*19

LOGISTIC REGRESSION VARIABLES candlik_likerpcRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons 
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

*20

LOGISTIC REGRESSION VARIABLES candlik_dislrpcRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons 
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

*	21	congapp_job_x	PRE: SUMMARY- Approval of Congress	Likert - Branch

Freq congapp_job_x.
MISSING VALUES congapp_job_x ().
RECODE congapp_job_x (1=4) (2=3)  (4=2) (5=1) INTO CongAppr.
VALUE LABELS
CongAppr
1 'Disapprove strongly'
2 'Disapprove'
3 'Approve'
4 'Approve stongly'.

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT CongAppr
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	22	presapp_track	PRE: Are things in the country on right track	Dichotomous

Freq presapp_track.
RECODE presapp_track (1=1) (2=0) INTO presapp_trackRC.
VALUE LABELS
presapp_trackRC
0 'Wrong Track'
1 'Right Track'.
Freq presapp_trackRC.

LOGISTIC REGRESSION VARIABLES presapp_trackRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons 
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

*	23	presapp_job_x	PRE: SUMMARY- Approval of President handling of job	Likert - Branch
*	24	presapp_econ_x	PRE: SUMMARY- Approval of President handling economy	Likert - Branch
*	25	presapp_foreign_x	PRE: SUMMARY- Approval of President handling foreign relations	Likert - Branch
*	26	presapp_health_x	PRE: SUMMARY- Approval of President handling health care	Likert - Branch
*	27	presapp_war_x	PRE: SUMMARY- Approval of President handling war in Afghanistan	Likert - Branch

Freq presapp_job_x presapp_econ_x presapp_foreign_x presapp_health_x presapp_war_x.
RECODE presapp_job_x presapp_econ_x presapp_foreign_x presapp_health_x presapp_war_x
(1=4) (2=3)  (4=2) (5=1) INTO PresAppJob PresAppEcon PresAppForeign PresAppHealth PresAppWar.
VALUE LABELS
PresAppJob PresAppEcon PresAppForeign PresAppHealth PresAppWar
1 'Disapprove strongly'
2 'Disapprove'
3 'Approve'
4 'Approve stongly'.
Freq PresAppJob PresAppEcon PresAppForeign PresAppHealth PresAppWar.

*23

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT PresAppJob
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*24

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT PresAppEcon
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*25

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT PresAppForeign
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*26

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT PresAppHealth
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*27

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT PresAppWar
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	28	ft_dpc	PRE: Feeling Thermometer: Democratic Presidential cand	Therm
*	29	ft_rpc	PRE: Feeling Thermometer: Republican Presidential cand	Therm
*	30	ft_dvpc	PRE: Feeling Thermometer: Democratic Vice-Pres cand	Therm
*	31	ft_rvpc	PRE: Feeling Thermometer: Republican Vice-Pres cand	Therm
*	32	ft_hclinton	PRE: Feeling Thermometer: Hillary Clinton	Therm
*	33	ft_gwb	PRE: Feeling Thermometer: GW Bush	Therm
*	34	ft_dem	PRE: Feeling Thermometer: Democratic Party	Therm
*	35	ft_rep	PRE: Feeling Thermometer: Republican Party	Therm

Freq ft_dpc ft_rpc ft_dvpc ft_rvpc ft_hclinton ft_gwb ft_dem ft_rep.
MISSING VALUES ft_dpc ft_rpc ft_dvpc ft_rvpc ft_hclinton ft_gwb ft_dem ft_rep (-9, -8,-2).

*28

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT ft_dpc
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*29

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT ft_rpc
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*30

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT ft_dvpc
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*31

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT ft_rvpc
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*32

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT ft_hclinton
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*33

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT ft_gwb
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*34

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT ft_dem
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*35

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT ft_rep
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	36	ptylik_likdp	PRE: Is there anything R likes about Democratic Party	Dichotomous
*	37	ptylik_disldp	PRE: Is there anything R dislikes about Democratic Party	Dichotomous
*	38	ptylik_likrp	PRE: Is there anything R likes about Republican Party	Dichotomous
*	39	ptylik_dislrp	PRE: Is there anything R dislikes about Republican Party	Dichotomous

Freq ptylik_likdp ptylik_disldp ptylik_likrp ptylik_dislrp.
RECODE ptylik_likdp ptylik_disldp ptylik_likrp ptylik_dislrp (1=1) (2=0) INTO
LikeDem DisLikeDem LikeRep DisLikeRep.
Freq LikeDem DisLikeDem LikeRep DisLikeRep.

*36

LOGISTIC REGRESSION VARIABLES LikeDem
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons 
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

*37

LOGISTIC REGRESSION VARIABLES DisLikeDem
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons 
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

*38

LOGISTIC REGRESSION VARIABLES LikeRep
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons 
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

*39

LOGISTIC REGRESSION VARIABLES DisLikeRep
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons 
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

*	40	finance_finpast_x	PRE: SUMMARY- Better or worse off than 1 year ago	Likert - Branch
*	41	finance_finnext_x	PRE: SUMMARY- Better or worse off 1 year from now	Likert - Branch

Freq  finance_finpast_x finance_finnext_x.
RECODE finance_finpast_x finance_finnext_x (1=5) (2=4) (3=3) (4=2) (5=1) INTO PastFinances FutureFinances.
VALUE LABELS 
PastFinances FutureFinances
1 'Much worse'
2 'Somewhat worse'
3 'Same'
4 'Somewhat better'
5 'Much better'.
Freq PastFinances FutureFinances.

*40 

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT PastFinances
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*41

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT FutureFinances
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	42	health_2010hcr_x	PRE: SUMMARY- Support 2010 health care law	Likert - Branch

Freq health_2010hcr_x.
RECODE health_2010hcr_x (1=7) (2=6) (3=5) (4=4) (5=3) (6=2) (7=1) INTO ACAAppr.
VALUE LABELS
ACAAppr
1 'Oppose great deal'
2 'Oppose moderately'
3 'Oppose a little'
4 'Neither'
5 'Favor a little'
6 'Favor moderately'
7 'Favor a great deal'.
Freq ACAAppr.

*42

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT ACAAppr
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	43	candaff_angdpc	PRE: Affect for Democratic Pres cand: angry	Dichotomous
*	44	candaff_angdpcoft	PRE: How often affect angry about Democratic Pres cand	Likert
*	45	candaff_hpdpc	PRE: Affect for Democratic Pres cand: hopeful	Dichotomous
*	46	candaff_hpdpcoft	PRE: How often affect hopeful about Democratic Pres cand	Likert
*	47	candaff_afrdpc	PRE: Affect for Democratic Pres cand: afraid	Dichotomous
*	48	candaff_afrdpcoft	PRE: How often affect afraid about Democratic Pres cand	Likert
*	49	candaff_prddpc	PRE: Affect for Democratic Pres cand: proud	Dichotomous
*	50	candaff_prddpcoft	PRE: How often affect proud about Democratic Pres cand	Likert
*	51	candaff_angrpc	PRE: Affect for Republican Pres cand: angry	Dichotomous
*	52	candaff_angrpcoft	PRE: How often affect angry about Republican Pres cand	Likert
*	53	candaff_hprpc	PRE: Affect for Republican Pres cand: hopeful	Dichotomous
*	54	candaff_hprpcoft	PRE: How often affect hopeful about Republican Pres cand	Likert
*	55	candaff_afrrpc	PRE: Affect for Republican Pres cand: afraid	Dichotomous
*	56	candaff_afrrpcoft	PRE: How often affect afraid about Republican Pres cand	Likert
*	57	candaff_prdrpc	PRE: Affect for Republican Pres cand: proud	Dichotomous
*	58	candaff_prdrpcoft	PRE: How often affect proud about Republican Pres cand	Likert

Freq candaff_angdpc candaff_hpdpc candaff_afrdpc candaff_prddpc
candaff_angrpc candaff_hprpc candaff_afrrpc candaff_prdrpc.
RECODE candaff_angdpc candaff_hpdpc candaff_afrdpc candaff_prddpc
candaff_angrpc candaff_hprpc candaff_afrrpc candaff_prdrpc (1=1) (2=0) INTO 
candaff_angdpcRC candaff_hpdpcRC candaff_afrdpcRC candaff_prddpcRC
candaff_angrpcRC candaff_hprpcRC candaff_afrrpcRC candaff_prdrpcRC.
Freq candaff_angdpcRC candaff_hpdpcRC candaff_afrdpcRC candaff_prddpcRC
candaff_angrpcRC candaff_hprpcRC candaff_afrrpcRC candaff_prdrpcRC.

Freq candaff_angdpcoft candaff_hpdpcoft candaff_afrdpcoft candaff_prddpcoft
candaff_angrpcoft candaff_hprpcoft candaff_afrrpcoft candaff_prdrpcoft.
RECODE candaff_angdpcoft candaff_hpdpcoft candaff_afrdpcoft candaff_prddpcoft
candaff_angrpcoft candaff_hprpcoft candaff_afrrpcoft candaff_prdrpcoft 
(1=5) (2=4) (3=3) (4=2) (5=1) INTO 
candaff_angdpcoftRC candaff_hpdpcoftRC candaff_afrdpcoftRC candaff_prddpcoftRC
candaff_angrpcoftRC candaff_hprpcoftRC candaff_afrrpcoftRC candaff_prdrpcoftRC.
VALUE LABELS
candaff_angdpcoftRC candaff_hpdpcoftRC candaff_afrdpcoftRC candaff_prddpcoftRC
candaff_angrpcoftRC candaff_hprpcoftRC candaff_afrrpcoftRC candaff_prdrpcoftRC 
1 'Never'
2 'Some of the time'
3 'About half the time'
4 'Most of the time'
5 'Always'.
Freq candaff_angdpcoftRC candaff_hpdpcoftRC candaff_afrdpcoftRC candaff_prddpcoftRC
candaff_angrpcoftRC candaff_hprpcoftRC candaff_afrrpcoftRC candaff_prdrpcoftRC .

*43

LOGISTIC REGRESSION VARIABLES candaff_angdpcRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons 
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

*44

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT candaff_angdpcoftRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*45

LOGISTIC REGRESSION VARIABLES candaff_hpdpcRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons 
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

*46

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT candaff_hpdpcoftRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*47

LOGISTIC REGRESSION VARIABLES candaff_afrdpcRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons 
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

*48

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT candaff_afrdpcoftRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*49

LOGISTIC REGRESSION VARIABLES candaff_prddpcRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons 
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

*50

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT candaff_prddpcoftRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*51

LOGISTIC REGRESSION VARIABLES candaff_angrpcRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons 
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

*52

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT candaff_angrpcoftRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*53

LOGISTIC REGRESSION VARIABLES candaff_hprpcRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons 
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

*54

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT candaff_hprpcoftRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*55

LOGISTIC REGRESSION VARIABLES candaff_afrrpcRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons 
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

*56

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT candaff_afrrpcoftRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*57

LOGISTIC REGRESSION VARIABLES candaff_prdrpcRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons 
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

*58

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT candaff_prdrpcoftRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	59	libcpre_dpc	PRE: 7pt scale liberal/conservative - Dem Pres cand	LibCon
*	60	libcpre_rpc	PRE: 7pt scale liberal/conservative - Rep Pres cand	LibCon
*	61	libcpre_ptyd	PRE: 7pt scale liberal/conservative Dem party	LibCon
*	62	libcpre_ptyr	PRE: 7pt scale liberal/conservative Rep party	LibCon

Freq libcpre_dpc libcpre_rpc libcpre_ptyd libcpre_ptyr.
MISSING VALUES libcpre_dpc libcpre_rpc libcpre_ptyd libcpre_ptyr (-9, -8).

*59

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT libcpre_dpc
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*60

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT libcpre_rpc
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*61

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT libcpre_ptyd
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*62

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT libcpre_ptyr
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	63	likelypct_howlikvt1	PRE: [1/2 SAMPLE 2A] How likely is it that R will vote in Nov	Likert

Freq likelypct_howlikvt1 .
RECODE likelypct_howlikvt1 (1=5) (2=4) (3=3) (4=2) (5=1) INTO LikelihoodVote.
VALUE LABELS 
LikelihoodVote
1 'Not likely at all'
2 'Slightly likely'
3 'Moderately likely'
4 'Very likely'
5 'Extremely likely'.
Freq LikelihoodVote.

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT LikelihoodVote
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	64	campfin_limcorp	PRE: Should gov be able to limit corporate contributions	Likert - Middle at End
*	65	campfin_banads	PRE: Ban corporate/union ads for candidates	Likert - Middle at End

Freq campfin_limcorp campfin_banads.
RECODE  campfin_limcorp campfin_banads (1=3) (2=1) (3=2) INTO campfin_limcorpRC campfin_banadsRC.
VALUE LABELS
campfin_limcorpRC campfin_banadsRC
1 'Oppose'
2 'Neither'
3 'Favor'.
Freq campfin_limcorpRC campfin_banadsRC.

*64

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT campfin_limcorpRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*65

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT campfin_banadsRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	66	ineq_incgap_x	PRE: SUMMARY- Income gap size compared to 20 years ago	Likert - Branch

Freq ineq_incgap_x.
RECODE ineq_incgap_x (1=5) (2=4) (3=3) (4=2) (5=1) INTO IncomeGapComparison.
VALUE LABELS
IncomeGapComparison
1 'Much smaller'
2 'Somewhat smaller'
3 'About the same'
4 'Somewhat larger'
5 'Much larger'.
Freq IncomeGapComparison.

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT IncomeGapComparison
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	67	effic_complicstd	PRE: [STD] Politics/govt too complicated to understand	Likert
*	68	effic_undstd	PRE: [STD] Good understanding of political issues	Likert
*	69	effic_carestd	PRE: [STD] Publ officials don't care what people think	Likert
*	70	effic_saystd	PRE: [STD] Have no say about what govt does	Likert
*	71	effic_complicrev	PRE: [REV] Politics/govt too complicated to understand	Likert
*	72	effic_undrev	PRE: [REV] Good understanding of political issues	Likert
*	73	effic_carerev	PRE: [REV] Publ officials don't care what people think	Likert
*	74	effic_sayrev	PRE: [REV] Have no say about what govt does	Likert

Freq effic_complicstd effic_undstd effic_carestd effic_saystd.
RECODE effic_complicstd effic_undstd effic_carestd effic_saystd
(1=5) (2=4) (3=3) (4=2) (5=1) INTO 
effic_complicstdRC effic_undstdRC effic_carestdRC effic_saystdRC.
VALUE LABELS
effic_complicstdRC effic_undstdRC effic_carestdRC effic_saystdRC
1 'Disagree strongly'
2 'Disagree'
3 'Neither'
4 'Agree'
5 'Agree strongly'.
Freq effic_complicstdRC effic_undstdRC effic_carestdRC effic_saystdRC.

Freq effic_complicrev effic_undrev effic_carerev effic_sayrev.
RECODE effic_complicrev effic_undrev effic_carerev effic_sayrev 
(1=5) (2=4) (3=3) (4=2) (5=1) INTO 
effic_complicrevRC effic_undrevRC effic_carerevRC effic_sayrevRC.
VALUE LABELS
effic_complicrevRC effic_undrevRC effic_carerevRC effic_sayrevRC
1 'Not at all'
2 'A little'
3 'A moderate amount'
4 'A lot'
5 'A great deal'.
Freq effic_complicrevRC effic_undrevRC effic_carerevRC effic_sayrevRC.

*67

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT effic_complicstdRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*68

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT effic_undstdRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*69

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT effic_carestdRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*70

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT effic_saystdRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*71

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT effic_complicrevRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*72

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT effic_undrevRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*73

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT effic_carerevRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*74

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT effic_sayrevRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	75	econ_ecnow	PRE: Current economy good or bad	Likert
*	76	econ_ecpast_x	PRE: SUMMARY- U.S. economy better or worse than 1 year ago	Likert - Branch
*	77	econ_ecnext_x	PRE: SUMMARY- U.S. economy better or worse 1 year from now	Likert - Branch
*	78	econ_unpast_x	PRE: SUMMARY- Unemployment better or worse than 1 year ago	Likert - Branch
*	79	econ_unnext	PRE: More or less unemployment in next year	Likert

*75

Freq econ_ecnow.
RECODE econ_ecnow (1=5) (2=4) (3=3) (4=2) (5=1) INTO CurrentEconomy.
VALUE LABELS 
CurrentEconomy
1 'Very bad'
2 'Bad'
3 'Neither'
4 'Good'
5 'Very good'.
Freq CurrentEconomy.

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT CurrentEconomy
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*76

Freq econ_unnext.
RECODE econ_unnext (1=3) (2=2) (3=1) INTO UnemployNextYear.
VALUE LABELS 
UnemployNextYear
1 'Less'
2 'About the same'
3 'More'.
Freq UnemployNextYear.

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT UnemployNextYear
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*77 78 79

Freq econ_ecpast_x econ_ecnext_x econ_unpast_x.
RECODE econ_ecpast_x econ_ecnext_x econ_unpast_x
(1=5) (2=4) (3=3) (4=2) (5=1) INTO 
econ_ecpast_xRC econ_ecnext_xRC econ_unpast_xRC.
VALUE LABELS
econ_ecpast_xRC econ_ecnext_xRC econ_unpast_xRC
1 'Much worse'
2 'Somewhat worse'
3 'Same'
4 'Somewhat better'
5 'Much better'.
Freq econ_ecpast_xRC econ_ecnext_xRC econ_unpast_xRC.

*77

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT econ_ecpast_xRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*78

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT econ_ecnext_xRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*79

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT econ_unpast_xRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	80	ecblame_pres	PRE: How much is President to blame for poor econ conditions	Likert
*	81	ecblame_fmpr	PRE: How much former President to blame for poor econ conds	Likert
*	82	ecblame_dem	PRE: How much Dems in Congress to blame for poor econ conds	Likert
*	83	ecblame_rep	PRE: How much Reps in Congress to blame for poor econ conds	Likert
*	84	ecblame_bank	PRE: How much Wall St. bankers to blame for poor econ conds	Likert
*	85	ecblame_cons	PRE: How much consumers to blame for poor economic conds	Likert
*	86	ecblame_lend	PRE: How much lenders to blame for poor economic conditions	Likert

Freq ecblame_pres ecblame_fmpr ecblame_dem ecblame_rep ecblame_bank ecblame_cons ecblame_lend.
RECODE ecblame_pres ecblame_fmpr ecblame_dem ecblame_rep ecblame_bank ecblame_cons ecblame_lend
(1=5) (2=4) (3=3) (4=2) (5=1) INTO 
ecblame_presRC ecblame_fmprRC ecblame_demRC ecblame_repRC ecblame_bankRC ecblame_consRC ecblame_lendRC.
VALUE LABELS
ecblame_presRC ecblame_fmprRC ecblame_demRC ecblame_repRC ecblame_bankRC ecblame_consRC ecblame_lendRC
1 'Not at all'
2 'A little'
3 'A moderate amount'
4 'A lot'
5 'A great deal'.
Freq ecblame_presRC ecblame_fmprRC ecblame_demRC ecblame_repRC ecblame_bankRC ecblame_consRC ecblame_lendRC.

*80

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT ecblame_presRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*81

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT ecblame_fmprRC 
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*82

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT ecblame_demRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*83

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT ecblame_repRC 
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*84

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT ecblame_bankRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*85

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT ecblame_consRC 
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*86

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT ecblame_lendRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	87	preswin_care	    PRE: Care who wins Presidential Election	Dichotomous
*	88	preswin_close	PRE: Will Pres race be a close or will (winner) win by a lot	Dichotomous
*	89	preswin_closest	PRE: Will Pres race be close in state	Dichotomous

Freq preswin_care preswin_close preswin_closest. 
RECODE preswin_care preswin_close preswin_closest (1=1) (2=0) INTO 
preswin_careRC preswin_closeRC preswin_closestRC.
Freq preswin_careRC preswin_closeRC preswin_closestRC.

*87

LOGISTIC REGRESSION VARIABLES preswin_careRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons 
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

*88

LOGISTIC REGRESSION VARIABLES preswin_closeRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons 
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

*89

LOGISTIC REGRESSION VARIABLES preswin_closestRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons 
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

*	90	preswin_dutychoice_x	PRE: SUMMARY- Does R consider voting as duty/choice	Likert - Branch

Freq preswin_dutychoice_x.
MISSING VALUES preswin_dutychoice_x (-9).
Freq preswin_dutychoice_x.

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT preswin_dutychoice_x
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	91	usworld_stronger	PRE: During last year, U.S. position in world weaker/str	Likert

Freq  usworld_stronger. 
MISSING VALUES usworld_stronger (-9).
Freq usworld_stronger.

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT usworld_stronger
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	92	usworld_stay	                        PRE: Country would be better off if we just stayed home	Dichotomous

Freq usworld_stay.
RECODE usworld_stay (1=1) (2=0) INTO usworld_stayRC.
Freq usworld_stayRC.

LOGISTIC REGRESSION VARIABLES usworld_stayRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons 
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

*	93	tea_supp_x	PRE: SUMMARY- Tea Party support	Likert - Branch

Freq tea_supp_x.
RECODE tea_supp_x (1=7) (2=6) (3=5) (4=4) (5=3) (6=2) (7=1) INTO tea_supp_xRC.
VALUE LABELS
tea_supp_xRC
1 'Strong opposition'
2 'Not very strong opposition'
3 'Lean opposing'
4 'Do not lean either way'
5 'Lean supporting'
6 'Not very strong supporting'
7 'Strong supporting'.
Freq tea_supp_xRC.

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT tea_supp_xRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	94	ctrait_dpcmoral	PRE: Pres Dem cand trait moral	Likert
*	95	ctrait_dpclead	PRE: Pres Dem cand trait strong leadership	Likert
*	96	ctrait_dpccare	PRE: Pres Dem cand trait really cares	Likert
*	97	ctrait_dpcknow	PRE: Pres Dem cand trait knowledgeable	Likert
*	98	ctrait_dpcint	                        PRE: Pres Dem cand trait intelligent	Likert
*	99	ctrait_dpchonst	PRE: Pres Dem cand trait honest	Likert
*	100	ctrait_rpcmoral	PRE: Pres Rep cand trait moral	Likert
*	101	ctrait_rpclead	PRE: Pres Rep cand trait strong leadership	Likert
*	102	ctrait_rpccare	PRE: Pres Rep cand trait really cares	Likert
*	103	ctrait_rpcknow	PRE: Pres Rep cand trait knowledgeable	Likert
*	104	ctrait_rpcint	                        PRE: Pres Rep cand trait intelligent	Likert
*	105	ctrait_rpchonst	PRE: Pres Rep cand trait honest	Likert

Freq ctrait_dpcmoral ctrait_dpclead ctrait_dpccare ctrait_dpcknow ctrait_dpcint ctrait_dpchonst.
RECODE ctrait_dpcmoral ctrait_dpclead ctrait_dpccare ctrait_dpcknow ctrait_dpcint ctrait_dpchonst
(1=5) (2=4) (3=3) (4=2) (5=1) INTO 
ctrait_dpcmoralRC ctrait_dpcleadRC ctrait_dpccareRC ctrait_dpcknowRC ctrait_dpcintRC ctrait_dpchonstRC.
VALUE LABELS
ctrait_dpcmoralRC ctrait_dpcleadRC ctrait_dpccareRC ctrait_dpcknowRC ctrait_dpcintRC ctrait_dpchonstRC
1 'Not well at all'
2 'Slightly well'
3 'Moderately well'
4 'Very well'
5 'Extremely well'.
Freq ctrait_dpcmoralRC ctrait_dpcleadRC ctrait_dpccareRC ctrait_dpcknowRC ctrait_dpcintRC ctrait_dpchonstRC.

Freq ctrait_rpcmoral ctrait_rpclead ctrait_rpccare ctrait_rpcknow ctrait_rpcint ctrait_rpchonst.
RECODE ctrait_rpcmoral ctrait_rpclead ctrait_rpccare ctrait_rpcknow ctrait_rpcint ctrait_rpchonst
(1=5) (2=4) (3=3) (4=2) (5=1) INTO 
ctrait_rpcmoralRC ctrait_rpcleadRC ctrait_rpccareRC ctrait_rpcknowRC ctrait_rpcintRC ctrait_rpchonstRC.
VALUE LABELS
ctrait_rpcmoralRC ctrait_rpcleadRC ctrait_rpccareRC ctrait_rpcknowRC ctrait_rpcintRC ctrait_rpchonstRC
1 'Not well at all'
2 'Slightly well'
3 'Moderately well'
4 'Very well'
5 'Extremely well'.
Freq ctrait_rpcmoralRC ctrait_rpcleadRC ctrait_rpccareRC ctrait_rpcknowRC ctrait_rpcintRC ctrait_rpchonstRC.

*94

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT ctrait_dpcmoralRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*95

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT ctrait_dpcleadRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*96

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT ctrait_dpccareRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*97

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT ctrait_dpcknowRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*98

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT ctrait_dpcintRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*99

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT ctrait_dpchonstRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*100

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT ctrait_rpcmoralRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*101

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT ctrait_rpcleadRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*102

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT ctrait_rpccareRC	
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*103

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT ctrait_rpcknowRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*104

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT ctrait_rpcintRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*105

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT ctrait_rpchonstRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	106	war_worthit	PRE: Was war worth the cost	Dichotomous

Freq  war_worthit.
RECODE war_worthit (1=1) (2=0) INTO war_worthitRC.
Freq war_worthitRC.

LOGISTIC REGRESSION VARIABLES war_worthitRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons 
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

*	107	war_terror	PRE: War increased or decreased threat of terrorism	Likert

Freq war_terror.
RECODE war_terror (1=3) (2=1) (3=2) INTO war_terrorRC.
VALUE LABELS
war_terrorRC
1 'Decreased'
2 'Stayed Same'
3 'Increased'.
Freq war_terrorRC.

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT war_terrorRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	108	spsrvpr_ssself	PRE: 7pt scale spending and Services self-placement	Likert
*	109	spsrvpr_ssdpc	PRE: 7pt scale spending and Services Dem Presidential cand	Likert
*	110	spsrvpr_ssrpc	PRE: 7pt scale spending and Services Rep Presidential cand	Likert
*	111	spsrvpr_ssdem	PRE: 7pt scale spending and Services Dem Party	Likert
*	112	spsrvpr_ssrep	PRE: 7pt scale spending and Services Rep Party	Likert

Freq spsrvpr_ssself spsrvpr_ssdpc spsrvpr_ssrpc spsrvpr_ssdem spsrvpr_ssrep.
MISSING VALUES spsrvpr_ssself spsrvpr_ssdpc spsrvpr_ssrpc spsrvpr_ssdem spsrvpr_ssrep (-9,-8,-2).

*108

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT spsrvpr_ssself
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*109

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT spsrvpr_ssdpc
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*110

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT spsrvpr_ssrpc
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*111

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT spsrvpr_ssdem
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*112

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT spsrvpr_ssrep
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	113	defsppr_self	PRE: 7pt scale defense spending self-placement	Likert
*	114	defsppr_dpc	PRE: 7pt scale defense spending Dem Pres cand	Likert
*	115	defsppr_rpc	PRE: 7pt scale defense spending Rep Pres cand	Likert
*	116	defsppr_dem	PRE: 7pt scale defense spending Dem Party	Likert
*	117	defsppr_rep	PRE: 7pt scale defense spending Rep Party	Likert

Freq defsppr_self defsppr_dpc defsppr_rpc defsppr_dem defsppr_rep.
MISSING VALUES defsppr_self defsppr_dpc defsppr_rpc defsppr_dem defsppr_rep (-9,-8,-2).

*113

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT defsppr_self
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*114

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT defsppr_dpc
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*115

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT defsppr_rpc
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*116

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT defsppr_dem
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*117

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT defsppr_rep
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	118	inspre_self	PRE: 7pt scale govt-private medical insur scale: self-plmt	Likert
*	119	inspre_dpc	PRE: 7pt scale govt-private medical insur scale: Dem Pres cand	Likert
*	120	inspre_rpc	PRE: 7pt scale govt-private medical insur scale: Rep Pres cand	Likert
*	121	inspre_dem	PRE: 7pt scale govt-private medical insur scale: Dem Party	Likert
*	122	inspre_rep	PRE: 7pt scale govt-private medical insur scale: Rep Party	Likert

Freq inspre_self inspre_dpc inspre_rpc inspre_dem inspre_rep.
MISSING VALUES inspre_self inspre_dpc inspre_rpc inspre_dem inspre_rep (-9,-8,-2).

*118

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT inspre_self
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*119

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT inspre_dpc
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*120

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT inspre_rpc
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*121

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT inspre_dem
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*122

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT inspre_rep
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	123	gun_control	                        PRE: Should fed govt make it more difficult to buy a gun	Likert

Freq gun_control.
RECODE gun_control (1=3) (2=1) (3=2) INTO gun_controlRC.
VALUE LABELS
gun_controlRC
1 'Easier'
2 'Keep Same'
3 'More difficult'.
Freq gun_controlRC.

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT gun_controlRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	124	gun_importance	PRE: Importance of gun access issue to R	Likert

Freq gun_importance.
RECODE gun_importance (1=5) (2=4) (3=3) (4=2) (5=1) INTO gun_importanceRC. 
VALUE LABELS
gun_importanceRC
1 'Not important at all'
2 'Not too important'
3 'Somewhat important'
4 'Very important'
5 'Extremely important'.
Freq gun_importanceRC.

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT gun_importanceRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	125	guarpr_self	PRE: 7pt scale guaranteed job-income scale: self-placement	Likert
*	126	guarpr_dpc	PRE: 7pt scale guaranteed job-income scale: Dem Pres cand	Likert
*	127	guarpr_rpc	PRE: 7pt scale guaranteed job-income scale: Rep Pres cand	Likert
*	128	guarpr_dem	PRE: 7pt scale guaranteed job-income scale: Dem Party	Likert
*	129	guarpr_rep	PRE: 7pt scale guaranteed job-income scale: Rep Party	Likert

Freq guarpr_self guarpr_dpc guarpr_rpc guarpr_dem guarpr_rep.
MISSING VALUES guarpr_self guarpr_dpc guarpr_rpc guarpr_dem guarpr_rep (-9,-8,-2).

*125 

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT guarpr_self
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*126

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT guarpr_dpc
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*127

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT guarpr_rpc
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*128

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT guarpr_dem
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*129 

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT guarpr_rep
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	130	immig_citizen	PRE: Opinion on proposal to allow citzship to some illeg aliens	Likert - Middle at End
*	131	immig_checks	PRE: Opinion on laws to allow immigr status checks on suspects	Likert - Middle at End

Freq immig_citizen immig_checks.
RECODE immig_citizen immig_checks (1=3) (2=1) (3=2) INTO immig_citizenRC immig_checksRC.
VALUE LABELS
immig_citizenRC immig_checksRC
1 'Oppose'
2 'Neither favor or oppose'
3 'Favor'.
Freq immig_citizenRC immig_checksRC.

*130

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT immig_citizenRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*131

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT immig_checksRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	132	aidblack_self	PRE: 7pt scale govt assistance to blacks scale: self-placemt	Likert
*	133	aidblack_dpc	PRE: 7pt scale govt assistance to blacks scale: Dem Pres cand	Likert
*	134	aidblack_rpc	PRE: 7pt scale govt assistance to blacks scale: Rep Pres cand	Likert

Freq aidblack_self aidblack_dpc aidblack_rpc.
MISSING VALUES aidblack_self aidblack_dpc aidblack_rpc (-9,-8,-2).

*132

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT aidblack_self
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*133

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT aidblack_dpc
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*134

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT aidblack_rpc
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	135	envjob_self	PRE: 7pt scale environment-jobs tradeoff self-placement	Likert
*	136	envjob_dpc	PRE: 7pt scale environment-jobs tradeoff Dem Pres cand	Likert
*	137	envjob_rpc	PRE: 7pt scale environment-jobs tradeoff Rep Pres cand	Likert

Freq envjob_self envjob_dpc envjob_rpc.
MISSING VALUES envjob_self envjob_dpc envjob_rpc (-9,-8,-2).

*135

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT envjob_self
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*136

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT envjob_dpc
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*137

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT envjob_rpc
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	138	aa_uni_x	PRE: SUMMARY- Favor/oppose affirmative action in universities	Likert - Branch
*	139	aa_work_x	PRE: SUMMARY- Favor/oppose affirmative action at work	Likert - Branch

Freq aa_uni_x aa_work_x.
RECODE aa_uni_x aa_work_x (1=7) (2=6) (3=5) (4=4) (5=3) (6=2) (7=1) INTO aa_uni_xRC aa_work_xRC.
VALUE LABELS
aa_uni_xRC aa_work_xRC
1 'Oppose a great deal'
2 'Oppose moderately'
3 'Oppose a little'
4 'Neither favor or oppose'
5 'Favor a little'
6 'Favor moderately'
7 'Favor a great deal'.
Freq aa_uni_xRC aa_work_xRC.

*138

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT aa_uni_xRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*139

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT aa_work_xRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	140	fedspend_ss	PRE: Federal Budget Spending: Social Security	Likert - Middle at End
*	141	fedspend_schools	PRE: Federal Budget Spending: public schools	Likert - Middle at End
*	142	fedspend_scitech	PRE: Federal Budget Spending: science and technology	Likert - Middle at End
*	143	fedspend_crime	PRE: Federal Budget Spending: dealing with crime	Likert - Middle at End
*	144	fedspend_welfare	PRE: Federal Budget Spending: welfare programs	Likert - Middle at End
*	145	fedspend_child	PRE: Federal Budget Spending: child care	Likert - Middle at End
*	146	fedspend_poor	PRE: Federal Budget Spending: aid to the poor	Likert - Middle at End
*	147	fedspend_enviro	PRE: Federal Budget Spending: protecting the environment	Likert - Middle at End

Freq fedspend_ss fedspend_schools fedspend_scitech fedspend_crime
fedspend_welfare fedspend_child fedspend_poor fedspend_enviro.
RECODE fedspend_ss fedspend_schools fedspend_scitech fedspend_crime
fedspend_welfare fedspend_child fedspend_poor fedspend_enviro 
(1=3) (2=1) (3=2) INTO 
fedspend_ssRC fedspend_schoolsRC fedspend_scitechRC fedspend_crimeRC
fedspend_welfareRC fedspend_childRC fedspend_poorRC fedspend_enviroRC.
VALUE LABELS
fedspend_ssRC fedspend_schoolsRC fedspend_scitechRC fedspend_crimeRC
fedspend_welfareRC fedspend_childRC fedspend_poorRC fedspend_enviroRC
1 'Decreased'
2 'Keep same'
3 'Increase'.
Freq fedspend_ssRC fedspend_schoolsRC fedspend_scitechRC fedspend_crimeRC
fedspend_welfareRC fedspend_childRC fedspend_poorRC fedspend_enviroRC.

*140

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT fedspend_ss
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*141

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT fedspend_schools
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*142

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT fedspend_scitech
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*143

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT fedspend_crime
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*144

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT fedspend_welfare
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*145

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT fedspend_child
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*146

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT fedspend_poor
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*147

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT fedspend_enviro
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	148	trustgov_trustgrev	PRE: [REV] How often trust govt in Wash to do what is right	Likert

Freq trustgov_trustgrev.
RECODE trustgov_trustgrev (1=5) (2=4) (3=3) (4=2) (5=1) INTO trustgov_trustgrevRC.
VALUE LABELS
trustgov_trustgrevRC
1 'Never'
2 'Some of the time'
3 'About half the time'
4 'Most of the time'
5 'Always'.
Freq trustgov_trustgrevRC.

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT trustgov_trustgrevRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	149	trustgov_trustgstd	PRE: [STD] How often trust govt in Wash to do what is right	Likert

Freq trustgov_trustgstd.
RECODE trustgov_trustgstd (1 = 4) (2=3) (3=2) (4=1) INTO trustgov_trustgstdRC.
VALUE LABELS
trustgov_trustgstdRC
1 'Never'
2 'Only some of the time'
3 'Most of the time'
4 'Just about always'.
Freq trustgov_trustgstdRC.

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT trustgov_trustgstdRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	150	trustgov_bigintrst	PRE: Govt run by a few big interests or for benefit of all	Dichotomous

Freq trustgov_bigintrst.
RECODE trustgov_bigintrst (1=0) (2=1) INTO trustgov_bigintrstRC.
VALUE LABELS
trustgov_bigintrstRC
0 'Run by few big interests'
1 'For the benefit of all'.
Freq trustgov_bigintrstRC.

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT trustgov_bigintrstRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	151	trustgov_waste	PRE: Does government waste much tax money	Likert

Freq trustgov_waste.
RECODE trustgov_waste (1=3) (2=2) (3=1) INTO trustgov_wasteRC.
VALUE LABLES 
trustgov_wasteRC
1 'Dont waste very much'
2 'Waste some'
3 'Waste a lot'.
Freq trustgov_wasteRC.

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT trustgov_wasteRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	152	trustgov_corrpt	PRE: How many in government are corrupt	Likert

Freq trustgov_corrpt.
RECODE trustgov_corrpt (1=5) (2=4) (3=3) (4=2) (5=1) INTO trustgov_corrptRC.
VALUE LABELS
trustgov_corrptRC
1 'None'
2 'A few'
3 'About half'
4 'Most'
5 'All'.
Freq trustgov_corrptRC.

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT trustgov_corrptRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	153	trust_social	PRE: How often can people be trusted	Likert

Freq trust_social.
RECODE trust_social (1=5) (2=4) (3=3) (4=2) (5=1) INTO trust_socialRC.
VALUE LABELS
trust_socialRC
1 'Never'
2 'Some of the time'
3 'About half the time'
4 'Most of the time'
5 'Always'.
Freq trust_socialRC.

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT trust_socialRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	154	respons_elections	PRE: Elections make govt pay attention	Likert

Freq respons_elections.
RECODE respons_elections (1=3) (2=2) (3=1) INTO respons_electionsRC.
VALUE LABELS 
respons_electionsRC
1 'Not much'
2 'Some'
3 'A good deal'.
Freq respons_electionsRC.

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT respons_electionsRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	155	econcand_dwin	PRE: Economy better or worse if Dem Pres cand wins	Likert
*	156	econcand_rwin	PRE: Economy better or worse if Rep Pres cand wins	Likert

Freq econcand_dwin econcand_rwin.
RECODE econcand_dwin econcand_rwin (1=3) (2=2) (3=1) INTO econcand_dwinRC econcand_rwinRC.
VALUE LABELS 
econcand_dwinRC econcand_rwinRC
1 'Get worse'
2 'Stay about the same'
3 'Get better'.
Freq econcand_dwinRC econcand_rwinRC.

*155

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT econcand_dwinRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*156

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT econcand_rwinRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	157	envir_drill	PRE: Favor or oppose increased U.S. offshore drilling	Likert - Middle at End
Freq envir_drill.
RECODE envir_drill (1=3) (2=1) (3=2) INTO envir_drillRC.
VALUE LABELS
envir_drillRC
1 'Oppose'
2 'Neither'
3 'Favor'.
Freq envir_drillRC.

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT envir_drillRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	158	envir_nuke	PRE: Should US have more or fewer nuclear power plants	Likert - Middle at End

Freq envir_nuke.
RECODE envir_nuke (1=3) (2=1) (3=2) INTO envir_nukeRC.
VALUE LABELS
envir_nukeRC
1 'Fewer'
2 'The same number'
3 'More'.
Freq envir_nukeRC.

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT envir_nukeRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	159	envir_gwarm	PRE: Is global warming happening or not	Dichotomous

Freq envir_gwarm.
RECODE envir_gwarm (1=0) (2=1) INTO envir_gwarmRC.
VALUE LABELS
envir_gwarmRC
0 'Has probably been happening'
1 'Probably has not been happening'.
Freq envir_gwarmRC.

LOGISTIC REGRESSION VARIABLES envir_gwarmRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons 
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

*	160	envir_gwgood	PRE: Rising temperatures good or bad	Likert - Middle at End

Freq envir_gwgood.
RECODE envir_gwgood (1=3) (2=1) (3=2) INTO envir_gwgoodRC.
VALUE LABELS
envir_gwgoodRC
1 'Bad'
2 'Neither'
3 'Good'.
Freq envir_gwgoodRC.

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT envir_gwgoodRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	161	gayrt_discrev_x	PRE: SUMMARY REV version- favor laws against gays/lesbians job discrim	Likert - Branch
*	162	gayrt_discstd_x	PRE: SUMMARY STD version- favor laws against gays/lesbians job discrim	Likert - Branch
*	163	gayrt_milrev_x	PRE: SUMMARY REV version- allow gays/lesbians serve in US armed forces	Likert - Branch
*	164	gayrt_milstd_x	PRE: SUMMARY STD version- allow gays/lesbians serve in US armed forces	Likert - Branch

Freq gayrt_discrev_x gayrt_discstd_x.
RECODE gayrt_discrev_x gayrt_discstd_x (1=4) (2=3) (4=2) (5=1) INTO gayrt_discrev_xRC gayrt_discstd_xRC.
VALUE LABELS
gayrt_discrev_xRC gayrt_discstd_xRC
1 'Disapprove strongly'
2 'Disapprove not strongly'
3 'Approve not strongly'
4 'Approve strongly'.
Freq gayrt_discrev_xRC gayrt_discstd_xRC.

Freq gayrt_milrev_x gayrt_milstd_x.
RECODE gayrt_milrev_x gayrt_milstd_x (1=4) (2=3) (4=2) (5=1) INTO gayrt_milrev_xRC gayrt_milstd_xRC.
VALUE LABELS
gayrt_milrev_xRC gayrt_milstd_xRC
1 'Disapprove strongly'
2 'Disapprove not strongly'
3 'Approve not strongly'
4 'Approve strongly'.
Freq gayrt_milrev_xRC gayrt_milstd_xRC.

*161

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT gayrt_discrev_xRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*162

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT gayrt_discstd_xRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*163

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT gayrt_milrev_xRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*164

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT gayrt_milstd_xRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	165	gayrt_adopt	PRE: Should gay and lesbian couples be allowed to adopt	Dichotomous

Freq gayrt_adopt.
RECODE gayrt_adopt (1=1) (2=0) INTO gayrt_adoptRC.
VALUE LABELS
gayrt_adoptRC
0 'No'
1 'Yes'.
Freq gayrt_adoptRC.

LOGISTIC REGRESSION VARIABLES gayrt_adoptRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons 
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

*	166	penalty_favopp_x	PRE: SUMMARY- Favor death penalty	Likert - Branch

Freq penalty_favopp_x.
RECODE penalty_favopp_x (1=4) (2=3) (4=2) (5=1) INTO penalty_favopp_xRC.
VALUE LABELS
penalty_favopp_xRC
1 'Disapprove strongly'
2 'Disapprove not strongly'
3 'Approve not strongly'
4 'Approve strongly'.
Freq penalty_favopp_xRC.

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT penalty_favopp_xRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	167	presadm_secure	PRE: U.S. more or less secure than when Pres took office	Likert - Middle at End

Freq presadm_secure.
RECODE presadm_secure (1=3) (2=1) (3=2) INTO presadm_secureRC.
VALUE LABELS
presadm_secureRC
1 'Less secure'
2 'No change'
3 'More secure'.
Freq presadm_secureRC.

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT presadm_secureRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	168	pctlikely_whatpct2	PRE: [1/2 SAMPLE 2B] What is percent chance R will vote in Nov	Therm

Freq pctlikely_whatpct2.
MISSING VALUES pctlikely_whatpct2 (-9, -1).
DESCRIPTIVES pctlikely_whatpct2.

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT pctlikely_whatpct2
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	169	pctlikely_howlikvt2	PRE: [1/2 SAMPLE 2A] How likely is it that R will vote in Nov	Likert

Freq pctlikely_howlikvt2.
RECODE pctlikely_howlikvt2 (1=5) (2=4) (3=3) (4=2) (5=1) INTO pctlikely_howlikvt2RC.
VALUE LABELS
pctlikely_howlikvt2RC
1 'Not at all likely'
2 'Slightly likely'
3 'Moderately likely'
4 'Very likely'
5 'Extremely likely'.
Freq pctlikely_howlikvt2RC.

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT pctlikely_howlikvt2RC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	170	medsrc_campsrcs_tvnews	PRE: CASI/WEB Media srces R followed about the Pres: mention TV news	Dichotomous
*	171	medsrc_campsrcs_newsp	PRE: CASI/WEB Media srces R foll'd about the Pres: mention newspapers	Dichotomous
*	172	medsrc_campsrcs_tvtalk	PRE: CASI/WEB Media srces R foll'd abt the Pres: mention TV talk shows	Dichotomous
*	173	medsrc_campsrcs_inet	PRE: CASI/WEB Media sources R followed about the Pres: mention Intnet	Dichotomous
*	174	medsrc_campsrcs_radio	PRE: CASI/WEB Media sources R followed about the Pres: mention radio	Dichotomous

Freq medsrc_campsrcs_tvnews medsrc_campsrcs_newsp medsrc_campsrcs_tvtalk 
medsrc_campsrcs_inet medsrc_campsrcs_radio 

*170 

LOGISTIC REGRESSION VARIABLES medsrc_campsrcs_tvnews
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons 
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

*171

LOGISTIC REGRESSION VARIABLES medsrc_campsrcs_newsp
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons 
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

*172

LOGISTIC REGRESSION VARIABLES medsrc_campsrcs_tvtalk
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons 
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

*173

LOGISTIC REGRESSION VARIABLES medsrc_campsrcs_inet
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons 
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

*174

LOGISTIC REGRESSION VARIABLES medsrc_campsrcs_radio
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons 
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

*	175	medsrc_socmedia	PRE: CASI/WEB No.days in wk R uses soc media to lrn abt Pres election	Likert
*	176	medsrc_blogs	PRE: CASI/WEB No.days in week R uses blogs to learn abt Pres election	Likert

Freq medsrc_socmedia.
MISSING VALUES medsrc_socmedia (-9).
Freq medsrc_socmedia.

Freq medsrc_blogs.
MISSING VALUES medsrc_blogs (-9).
Freq medsrc_blogs.

*175

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT medsrc_socmedia
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*176

REGRESSION
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN
  /DEPENDENT medsrc_blogs
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons.

*	177	preknow_prestimes	PRE: CASI/WEB Polit knowledge: number times can be elected (incl DKRF)	Political Knowledge
*	178	preknow_sizedef	PRE: CASI/WEB Political knowledge: size of federal deficit (incl DKRF)	Political Knowledge
*	179	preknow_senterm	PRE: Years Senator Elected (incl DKRF)	Political Knowledge
*	180	preknow_medicare	PRE: CASI/WEB Political knowledge: What is Medicare (incl DKRF)	Political Knowledge
*	181	preknow_leastsp	PRE: CASI/WEB Political knowledge: program Fed govt spends (incl DKRF)	Political Knowledge

Freq preknow_prestimes.
RECODE preknow_prestimes (0 THRU 1=0) (2=1) (3 THRU HI = 0) INTO preknow_prestimesRC.
Freq preknow_sizedef.
RECODE preknow_sizedef (1=1) (2 THRU 3=0) INTO preknow_sizedefRC.
Freq preknow_senterm.
RECODE preknow_senterm (0 THRU 5 =0 ) (6=1) (7 THRU HI =0) INTO preknow_sentermRC.
Freq preknow_medicare.
RECODE preknow_medicare (1=1) (2 THRU 4 = 0) INTO preknow_medicareRC.
Freq preknow_leastsp.
RECODE preknow_leastsp (1=1) (2 THRU 4 = 0) INTO preknow_leastspRC.
Freq preknow_prestimesRC preknow_sizedefRC preknow_sentermRC preknow_medicareRC preknow_leastspRC.

*177

LOGISTIC REGRESSION VARIABLES preknow_prestimesRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons 
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

*178

LOGISTIC REGRESSION VARIABLES preknow_sizedefRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons 
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

*179

LOGISTIC REGRESSION VARIABLES preknow_sentermRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons 
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

*180

LOGISTIC REGRESSION VARIABLES preknow_medicareRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons 
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).

*181

LOGISTIC REGRESSION VARIABLES preknow_leastspRC
  /METHOD=ENTER NumberSurveys female agecats married educ working white Internet HHLandline incomePre Indep Rep Lib Mod Cons 
  /CRITERIA=PIN(.05) POUT(.10) ITERATE(20) CUT(.5).




*SAVE OUTFILE='/Users/Alex/Dropbox/Dissertation.2/Number of Surveys/Data/ANES '+
    '2012/anes_timeseries_2012 Substantive Variable Analysis File.sav'
  /COMPRESSED.

